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Bottom up vs. top down: What does firm 10-K tell us? 从下到上vs.从上到下：公司的10-K报表告诉我们什么？
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2026-04-13 DOI: 10.1016/j.finmar.2026.101070
Landon J. Ross , Jim Horn , Mert Pilanci , Kaihong Luo , Guofu Zhou
{"title":"Bottom up vs. top down: What does firm 10-K tell us?","authors":"Landon J. Ross ,&nbsp;Jim Horn ,&nbsp;Mert Pilanci ,&nbsp;Kaihong Luo ,&nbsp;Guofu Zhou","doi":"10.1016/j.finmar.2026.101070","DOIUrl":"10.1016/j.finmar.2026.101070","url":null,"abstract":"<div><div>While financial textual analysis increasingly relies on complex machine learning, we propose a simpler, data-driven alternative. Using elastic net regressions on a massive panel of 10-K n-grams, we construct a specialized dictionary that weights phrases by their marginal predictive power. This bottom-up methodology effectively forecasts expected stock returns, with a spread portfolio generating significant average returns. Our approach outperforms prominent financial dictionaries, off-the-shelf large language models, and machine learning algorithms. These results demonstrate the value of identifying financial meaning from the bottom up, highlighting the need for domain-specific models trained on relevant financial contexts.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101070"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Environmental sustainability and stock returns 环境可持续性和股票回报
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-08-19 DOI: 10.1016/j.finmar.2025.101006
William O. Brown , Xiaoli Gao , Yufeng Han , Dayong Huang , Fang Wang
{"title":"Environmental sustainability and stock returns","authors":"William O. Brown ,&nbsp;Xiaoli Gao ,&nbsp;Yufeng Han ,&nbsp;Dayong Huang ,&nbsp;Fang Wang","doi":"10.1016/j.finmar.2025.101006","DOIUrl":"10.1016/j.finmar.2025.101006","url":null,"abstract":"<div><div>We apply machine learning methods to granular environmental variables and test if there is a strong positive relation between environmental sustainability and future stock returns. A long-short portfolio that longs stocks with high forecasted returns and sells stocks with low forecasted returns earns large abnormal returns, and it performs better when climate concerns in the media are more intense. Further diagnosis shows that various dimensions of environmental sustainability help return predictions. High forecasted returns are associated primarily with strong environmental operational performance. The return prediction based on a customized transformer model is similar.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101006"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Estimating market liquidity from daily data: Marrying microstructure models and machine learning 从日常数据估计市场流动性：结合微观结构模型和机器学习
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-09-19 DOI: 10.1016/j.finmar.2025.101019
Yuehao Dai , Chao Shi , Ruixun Zhang
{"title":"Estimating market liquidity from daily data: Marrying microstructure models and machine learning","authors":"Yuehao Dai ,&nbsp;Chao Shi ,&nbsp;Ruixun Zhang","doi":"10.1016/j.finmar.2025.101019","DOIUrl":"10.1016/j.finmar.2025.101019","url":null,"abstract":"<div><div>We apply machine learning to estimate daily measures of market liquidity by combining microstructure models with low-frequency daily data only, in stock markets in the United States and China. Boosting trees and neural networks significantly improve the performance across different liquidity measures. Our machine learning models are interpretable and improvements are due to (a) more information from raw data that microstructure models do not capture; and (b) better use of information from learned nonlinear and non-monotonic relationships. We further demonstrate two applications of our trained machine learning models in estimating the illiquidity risk premium and systematic liquidity risk.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101019"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Technical indicators and the cross-section of corporate bond returns in a machine learning era 机器学习时代的技术指标和公司债券回报的横截面
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-11-06 DOI: 10.1016/j.finmar.2025.101029
Jern Tat Chin , Xu Guo , Hai Lin , Yi Mei
{"title":"Technical indicators and the cross-section of corporate bond returns in a machine learning era","authors":"Jern Tat Chin ,&nbsp;Xu Guo ,&nbsp;Hai Lin ,&nbsp;Yi Mei","doi":"10.1016/j.finmar.2025.101029","DOIUrl":"10.1016/j.finmar.2025.101029","url":null,"abstract":"<div><div>We explore the use of technical indicators to forecast corporate bond returns with various machine learning models. We show that technical indicators yield statistically significant and economically meaningful results, consistently outperforming bond characteristics. Although bond characteristics possess predictive power for bond returns, they do not provide incremental value beyond technical indicators across all bonds. Additionally, machine learning models do not offer substantial improvements over the benchmark linear model. These results underscore the significance of technical indicators in the corporate bond market.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101029"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
International corporate bond returns: Uncovering predictability using machine learning 国际公司债券回报：利用机器学习揭示可预测性
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-08-16 DOI: 10.1016/j.finmar.2025.101008
Delong Li , Lei Lu , Zhen Qi , Guofu Zhou
{"title":"International corporate bond returns: Uncovering predictability using machine learning","authors":"Delong Li ,&nbsp;Lei Lu ,&nbsp;Zhen Qi ,&nbsp;Guofu Zhou","doi":"10.1016/j.finmar.2025.101008","DOIUrl":"10.1016/j.finmar.2025.101008","url":null,"abstract":"<div><div>We examine the cross-sectional predictability of corporate bond returns using a novel international dataset and a set of machine learning techniques. We find strong predictability in both U.S. and non-U.S. markets, with differing predictive factors. Bonds in developed markets show greater integration with the U.S. market and stronger ties to equity markets. Predictive performance of machine learning models varies over time and is greater before the onset of the COVID-19 pandemic and during periods of deteriorating business conditions, reduced market liquidity, elevated investor sentiment, and heightened risk aversion. The results offer insights into bond pricing and global diversification opportunities.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101008"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Commodity sentiment in predicting index futures returns 商品情绪预测指数期货收益
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-10-20 DOI: 10.1016/j.finmar.2025.101025
Qunzi Zhang
{"title":"Commodity sentiment in predicting index futures returns","authors":"Qunzi Zhang","doi":"10.1016/j.finmar.2025.101025","DOIUrl":"10.1016/j.finmar.2025.101025","url":null,"abstract":"<div><div>I examine the impact of commodity sentiment on S&amp;P 500 Index futures returns. Using machine learning methods on nearly two million commodity-related news articles from nine international newspapers, I create sentiment proxies for major commodities and find the commodity positive sentiment demonstrates significant forecasting power, both in-sample and out-of-sample. This predictability remains significant across both short- and long-term horizons, under various market conditions, and after controlling for other investor sentiment indices and key market predictors. Asset allocation conditioned on commodity sentiment can yield substantial economic gains. Furthermore, commodity sentiment can influence index futures returns through the discount rate channel.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101025"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The AI and machine learning revolution in financial markets 金融市场的人工智能和机器学习革命
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2026-02-23 DOI: 10.1016/j.finmar.2026.101059
Clara Vega , Dacheng Xiu , Liyan Yang
{"title":"The AI and machine learning revolution in financial markets","authors":"Clara Vega ,&nbsp;Dacheng Xiu ,&nbsp;Liyan Yang","doi":"10.1016/j.finmar.2026.101059","DOIUrl":"10.1016/j.finmar.2026.101059","url":null,"abstract":"<div><div>This special issue on “Machine Learning, Generative AI, and Trading” explores how cutting-edge digital technologies are transforming financial markets. The featured papers identify novel predictors, from cryptocurrency order flow to granular environmental data, that often outperform traditional factors. They introduce adaptive methodologies like meta-learning for regime shifts, apply large language models to textual analysis, and identify the critical value of large language models and unstructured data. We offer some thoughts for future research directions, including the need for economic interpretation of machine-discovered patterns and the regulations on algorithms.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101059"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine+Heuristics: Nonlinear parametric portfolio policies with economic restrictions 机器+启发式：具有经济限制的非线性参数投资组合策略
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-07-22 DOI: 10.1016/j.finmar.2025.101001
Haitao Li , Chongfeng Wu , Chunyang Zhou
{"title":"Machine+Heuristics: Nonlinear parametric portfolio policies with economic restrictions","authors":"Haitao Li ,&nbsp;Chongfeng Wu ,&nbsp;Chunyang Zhou","doi":"10.1016/j.finmar.2025.101001","DOIUrl":"10.1016/j.finmar.2025.101001","url":null,"abstract":"<div><div>We use neural networks to model portfolio weights as nonlinear functions of firm characteristics. Applying our method to the equity market in the United States excluding micro-cap stocks, we find the long-only portfolio from the network with four hidden layers performs best and tends to have investments in stocks exhibiting low risk, high value, and high profitability, the characteristics that contribute significantly to the out-of-sample performance. While more complex networks are associated with increased turnovers and elevated trading costs, we find that autoregressive smoothing policies are effective in reducing portfolio turnover and improving model performance.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101001"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Meta-learning for return prediction in shifting market regimes 基于元学习的市场变化机制回报预测
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-06-01 Epub Date: 2025-11-29 DOI: 10.1016/j.finmar.2025.101042
Yicheng Wang , Sandro Claudio Lera
{"title":"Meta-learning for return prediction in shifting market regimes","authors":"Yicheng Wang ,&nbsp;Sandro Claudio Lera","doi":"10.1016/j.finmar.2025.101042","DOIUrl":"10.1016/j.finmar.2025.101042","url":null,"abstract":"<div><div>We propose a meta-learning framework for cross-sectional return prediction that adapts to regime-dependent dynamics. Instead of learning a fixed mapping from features to returns, we condition our model forecasts on recent feature-return relationships. This allows it to adjust to evolving market states without explicit regime labels or frequent re-estimation. We implement the framework with a Transformer-based Bayesian predictor, the Financial Prior-data Fitted Network (FinPFN), and evaluate it on daily Chinese A-shares and monthly U.S. equities. During regime changes, proxied by large volatility shifts, our method significantly outperforms benchmarks, offering a practical tool for dynamic return prediction.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"79 ","pages":"Article 101042"},"PeriodicalIF":2.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intraday proprietary traders and short-term mispricing 日内自营交易员和短期错误定价
IF 2.1 2区 经济学
Journal of Financial Markets Pub Date : 2026-03-01 Epub Date: 2025-11-05 DOI: 10.1016/j.finmar.2025.101028
V. Ravi Anshuman , Prachi Deuskar , Krishnamurthy V. Subramanian , Ramabhadran S. Thirumalai
{"title":"Intraday proprietary traders and short-term mispricing","authors":"V. Ravi Anshuman ,&nbsp;Prachi Deuskar ,&nbsp;Krishnamurthy V. Subramanian ,&nbsp;Ramabhadran S. Thirumalai","doi":"10.1016/j.finmar.2025.101028","DOIUrl":"10.1016/j.finmar.2025.101028","url":null,"abstract":"<div><div>Whose trading corrects temporary mispricing? We document that intraday proprietary traders play this crucial role. Using trader-level transaction data, we examine the activities of different types of traders around a media-driven, clean shock to retail trading. Intraday proprietary traders are contrarian and contribute to an immediate and sustained price correction. Their trades generate returns from informed trading rather than liquidity provision. Conversely, longer-term proprietary traders who engage in much less contrarian trading make small returns from liquidity provision, not informed trading. Thus, policies designed to curb short-termism must account for the contributions of different types of traders to price discovery.</div></div>","PeriodicalId":47899,"journal":{"name":"Journal of Financial Markets","volume":"78 ","pages":"Article 101028"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147570812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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